3190 results about "Time synchronization" patented technology

The invention relates to the technical field of elevator equipment state monitoring and fault diagnosis, and discloses an elevator traction machine running state real-time diagnosis method and system based on high-frequency sampling. According to the method, vibration (larger than or equal to 20 kHz), current (larger than or equal to 10 kHz), sound / sound emission, temperature and rotating speed signals of a traction machine are synchronously collected through a high-frequency multi-mode sensor array; capturing early weak fault transient characteristics; the edge computing unit completes data preprocessing, time synchronization, feature extraction and anomaly detection, and uploads key data to a cloud end through cloud-edge collaboration; the cloud end adopts a working condition self-adaptive strategy and a multi-modal fusion model to carry out deep diagnosis, and outputs fault types, positions and grades; and combining incremental learning and a degradation model to realize health quantification and residual life prediction. Through fusion of high-frequency data capture and an intelligent algorithm, the early fault detection capability, variable working condition adaptability and diagnosis real-time performance of the traction machine are improved, and a solution is provided for predictive maintenance of an elevator.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention provides an inertial navigation attitude resolving method and device based on sensor fusion. The method comprises the following steps: carrying out multi-clock domain synchronous calibration processing on a double-antenna GNSS system, an IMU inertial measurement unit and an antenna servo system in a vehicle-mounted communication-in-motion system; according to the time synchronization reference, carrying out quality evaluation and weight distribution processing on the GNSS satellite signal under the satellite communication in motion antenna pointing constraint; and carrying out multi-sensor fusion resolving processing on the inertial attitude of the vehicle body according to the time synchronization reference and the GNSS observation data weight distribution result. According to the method, millisecond-level time synchronization is realized by establishing a robust extended Kalman filtering model of a multi-dimensional extended state vector, GNSS signal quality is optimized by adopting an adaptive weight distribution strategy of antenna pointing constraint, and attitude fusion precision is enhanced by utilizing high-precision angle feedback of an antenna servo system. The problem that a traditional attitude resolving method in a vehicle-mounted communication-in-motion system is not high in precision is solved.

The invention relates to the technical field of high-precision satellite navigation and positioning, and discloses a multi-modal sensor space-time synchronization method for high-precision data acquisition, which comprises the following steps: constructing a hardware delay observation loop to calculate physical response delay in real time; establishing a receiver clock drift model and extracting a clock drift coefficient; generating a phase lead trigger instruction in response to the delay mean value and the clock drift coefficient cumulant based on the GNSS whole-second target moment; according to the method, a feed-forward control closed loop is constructed by using GNSS clock difference parameters, the intrinsic alignment of the heterogeneous sensor physical sampling action and GNSS atomic time is realized, the real-time alignment of the sensor physical sampling action and the GNSS atomic time is realized, and the real-time alignment of the sensor physical sampling action and the GNSS atomic time is realized. And a strict physical time reference is provided for high-dynamic multi-source data fusion.

The invention provides a property system virtual-real mapping inspection method, device and equipment based on digital twinning and a medium, and belongs to the technical field of property inspection. Real-time mapping of an equipment entity and a virtual model is achieved through layered design of a physical layer, a data layer, a twinning layer and an application layer; collecting static / dynamic data and reducing noise, and establishing a global coordinate system; constructing a 1: 1 parameterized model and binding equipment attributes; the state analysis model is used for evaluating the equipment health degree, and virtual-real identification and work order visualization are triggered when abnormity occurs; the inspection path is dynamically optimized, and the processing efficiency is improved in combination with AR assistance and dual-stage verification; and finally, updating the model weight and adjusting the inspection strategy through data-driven acceptance feedback. Real-time synchronization and intelligent decision making of the equipment state are realized, the inspection efficiency is improved, the fault omission ratio is reduced, the resource allocation is optimized, and the operation and maintenance transparency and reliability are enhanced.

The invention discloses an intelligent thickness control system for calendering a high-brightness edge sealing sheet, and relates to the technical field of intelligent manufacturing and industrial process control. The method sequentially comprises six steps of multi-modal online acquisition, multi-source fusion drift compensation, digital twinning real-time synchronization, model prediction closed-loop control, high-speed execution fault self-diagnosis and reinforcement learning adaptive optimization. Thickness data are synchronously obtained through laser, terahertz, ultrasound and environment quantity, trusted thickness flow is output through a drift model and a confidence matrix, future thickness is predicted through digital twinning rolling to give uncertainty, first-step control quantity is generated through quadratic programming, millisecond-level execution is conducted through a distributed clock, and self-healing is conducted under shadow driving hot standby and cloud diagnosis. And reinforcement learning agent online iteration improves the energy-saving and stable performance, and full-life-cycle closed-loop control with accurate thickness, low energy consumption and long service life of equipment under complex working conditions is realized.

The invention relates to a ship automatic berthing safety control system, and belongs to the technical field of port ship operation safety control. The system comprises four core modules which are sequentially connected to form closed-loop control: a situation synchronous sensing module generates total-factor dynamic attitude information through space-time synchronization and data fusion of a ship-shore inertial unit; the digital twin deduction module performs real-time simulation based on a ship six-degree-of-freedom motion model, and predicts a ship motion track and berth acting force in a prospective manner; the safety decision and early warning module compares the prediction track with the safe berthing corridor through a track-berthing force coupling algorithm, and outputs a cooperative control strategy; and the multi-terminal cooperative execution module converts the strategy into an instruction and synchronously distributes the instruction to a tug, a navigation bridge and a wharf system. Automatic closed-loop control of the whole process from sensing, prediction, decision making to execution is achieved, and the safety, accuracy and operation efficiency of ship berthing are effectively improved.

The invention discloses an intelligent file service system generation method based on a low-code development mode. The method comprises the following steps: constructing a file service core data model, defining file metadata fields, a storage structure and an association relationship, and forming a standardized data architecture. According to the method, the core data model, the visual page component library and the drag-and-drop workflow template are constructed, linkage binding of data-component-process is achieved in combination with a low-code engine, and the efficiency bottleneck of a traditional development mode is broken through; through a three-way real-time synchronization mechanism, real-time hot repair and dynamic branch judgment, page layout and flow rule adjustment can take effect immediately through visual configuration, and the problem of dynamic demand response lag is solved; through the intelligent resource scheduling logic compiled through environment adaptation, a differentiation scheme is generated for different deployment environments, the configuration difference influence under a private cloud and server mixed deployment scene is remarkably reduced, the deployment failure rate is greatly reduced, and the system online stability is improved.

A device for time-based tracking and cost optimization in construction projects, the device comprising the following: a robust housing suitable for use on construction sites; a processing unit located inside the housing, configured to perform real-time time-stamping, data acquisition and preprocessing tasks; a multimodal sensor unit that is operationally coupled with the processing unit, wherein the sensor unit comprises at least a motion sensor, an RFID reader, sensors for environmental conditions and a vision module with optical character recognition; a real-time clock module that is operationally connected to the processing unit to provide time synchronization for all sensor data streams; a wireless communication module that supports the Wi-Fi, LoRa and LTE protocols and is configured for transmitting time-stamped data to a central project server; a storage module that is operationally coupled with the processing unit to locally buffer time series data of construction activity during offline operation; a housing-mounted, touchscreen-based human-machine interface configured to allow site personnel to enter activity updates and confirm the status of construction tasks; a cost optimization engine running on the central server, the engine being configured to receive time-synchronized sensor data from multiple such devices and dynamically calculate time-cost trade-offs using a predictive planning technique that incorporates the principles of the critical path and the power value; furthermore, the device is configured to be integrated into a digital twin environment of the building under construction in order to provide real-time visualization of progress and to generate suggestions for resource reallocation based on a time-cost-benefit analysis.

The invention relates to the technical field of audio playing optimization, and discloses a low-delay multi-room audio playing optimization method and system, a storage medium and equipment, and the method comprises the steps: building a global time synchronization system based on a precision clock protocol in a playing equipment cluster, and enabling all playing equipment to operate based on a unified time reference; monitoring network state parameters in real time, dynamically calculating and adjusting the audio buffer depth of each playing device based on the monitored network quality, and obtaining audio data in advance according to the playing progress and the network state; generating a global playing timestamp according to the global time synchronization system, and controlling all playing devices to perform synchronous starting and progress control of audio playing based on the global playing timestamp; a transmission strategy of audio data is dynamically optimized based on real-time network state parameters, packet loss recovery and bandwidth adaptation are achieved, sub-millisecond multi-room audio synchronous playing is achieved, and meanwhile performance parameters can be automatically optimized according to different network environments and hardware configurations.

The invention discloses a behavior-driven twinborn prediction method, and relates to the technical field of intelligent information processing and prediction modeling, and the method comprises the following steps: building a unified event time baseline, collecting nanosecond clock offset information of each data source, building a cross time sequence suspicion chart, recognizing time synchronization abnormal nodes, and forming a credible time anchor point set; and based on the trusted time anchor point set, executing anti-fact playback, reconstructing a historical evolution process of behavior data, generating a time offset vector set, and constructing a corresponding time sequence offset spectrum. According to the method, through construction of a unified time baseline, trusted anchor points, anti-fact replay, causal topology and time reversal control, time sequence dislocation identification, calibration and false trajectory elimination of multi-source behavior data are realized, a dynamic self-healing closed-loop prediction mechanism is established, and the twin system prediction accuracy and stability are improved.

The invention discloses a ground wire multi-machine synchronous traction pay-off scheduling method and system, relates to the technical field of high-voltage transmission line construction, and is used for solving the problem of insufficient multi-machine real-time tension synchronization precision in the existing method. A tension sensor and a speed sensor which are arranged on each traction device collect a tension value, a traction speed value and position coordinate data of a ground wire in real time, and transmit the data to a central scheduling controller; establishing a multi-machine unified time synchronization reference and tension reference model, calibrating a timestamp through a GPS module, and initializing a threshold value based on historical parameters; comparing tension values one by one to calculate a deviation vector sequence; activating a compensation algorithm to execute a speed fine adjustment and tension compensation instruction; iteratively calculating the feedback adjustment value until the deviation is stable; and integrating the synchronous data to the overall paying-off scheduling model to dynamically update the path planning and speed curve. According to the method and the system, the problem of insufficient multi-machine real-time tension synchronization precision in the prior art is effectively solved.

The invention discloses a real-time interaction and intelligent management method for a multi-dimensional data space, and relates to the technical field of data interaction management, and the method comprises the following steps: I, collecting multi-source data, including structured data, unstructured data and spatio-temporal data, in real time through a diversified collection terminal, and dynamically adjusting the sampling frequency based on a data entropy value, carrying out cross-device space-time synchronization calibration and edge end lightweight processing on the acquired data; iI, preprocessing the data flow in real time, fusing the multi-modal data, and the like. According to the method, the sampling frequency is dynamically adjusted based on the data entropy value, and in combination with edge end lightweight processing, invalid data transmission is reduced, and the key data acquisition efficiency is improved; hot data is stored in a time sequence database and a memory database, real-time monitoring of an emergency scene and second-level response of emergency command are supported, dynamic capacity expansion and shrinkage of computing resources are matched, edge-cloud end collaboratively distributes tasks, network delay is reduced, and system stability and response speed in a high-concurrency scene are guaranteed.

The invention relates to the technical field of partial discharge of power equipment, and particularly discloses a multi-modal sensing-based partial discharge monitoring and positioning system for power equipment, and the system comprises a multi-modal sensing unit which is configured to be arranged around to-be-monitored power equipment and is used for synchronously collecting various physical signals generated by partial discharge, the multi-mode sensing unit at least comprises an ultrahigh frequency sensing array used for receiving ultrahigh frequency electromagnetic wave signals generated by partial discharge; the ultrasonic sensing array is used for receiving ultrasonic signals generated by partial discharge; and the data acquisition and synchronization unit is connected with the multi-mode sensing unit. According to the power equipment partial discharge monitoring and positioning system based on multi-modal sensing, an integrated framework of multi-modal sensing cooperation and high-precision time synchronization is constructed, so that the problems of insufficient multi-modal cooperation, low time synchronization precision and no closed-loop optimization mechanism in existing partial discharge monitoring are effectively solved.

The invention discloses a time synchronization method suitable for a satellite communication system, and the method comprises the steps: analyzing a forward broadcast signal, and obtaining the reference time of a network clock; constructing a geometric prediction handle based on satellite orbit data, and predicting one-way propagation time delay between a terminal and a satellite by using the geometric prediction handle; estimating the processing delay of the terminal according to the symbol rate and the equipment working condition of the terminal and the delay calibration data; fusing the network clock reference time with the processing delay estimate to synthesize a terminal local time; and the sending time of the return link is calculated based on the local time and the propagation delay prediction. According to the method, the synchronization precision can be improved to a microsecond level, the convergence speed in a dynamic scene is accelerated, adaptive compression of the guard interval is supported, and the stability and spectrum efficiency of a satellite communication system are effectively improved.

The invention belongs to the technical field of computers, particularly relates to a digital twin power plant infrastructure multi-source heterogeneous data real-time fusion method, and aims to solve the problems of high data fusion delay, semantic segmentation and poor system adaptability in the prior art. The method comprises the following steps: constructing a unified space-time reference frame to realize nanosecond-level time synchronization and space coordinate normalization; the method comprises the following steps: accessing and preprocessing multi-source data such as a building information model, an Internet of Things sensor, a construction log and a video stream, and generating a standardization unit with space-time metadata; performing semantic analysis and cross-modal feature alignment based on the power plant infrastructure ontology knowledge base; and millisecond-level dynamic fusion is realized by adopting an event-triggered streaming engine. According to the scheme, real-time fusion within 100 milliseconds is realized, the semantic alignment precision is 98% or above, the state confidence is 90% or above, the system throughput is improved by three times by relying on a cloud edge collaborative architecture, and precise twin mapping and intelligent decision making of the whole process of power plant infrastructure construction are comprehensively supported.

The invention relates to the technical field of data integration, in particular to a multi-source data integration system. According to the technical scheme, the system comprises a plurality of heterogeneous data acquisition devices, a data processing module, a data storage module and a data transmission module. Differentiation processing is carried out on data of different devices, accurate time synchronization and data alignment are achieved through an NTP protocol and dynamic calibration, intelligent noise processing and data cleaning are carried out through multiple algorithms, compression encryption is adopted in storage, and a double-queue priority and breakpoint resume mechanism is applied in transmission. And fault monitoring and automatic processing are realized by means of hierarchical log recording and intelligent analysis, so that the problems of data isomerism, time synchronization, noise interference, storage and transmission efficiency, system reliability and the like are efficiently solved, and efficient, stable and safe data support is provided for a real-time monitoring platform.

The invention discloses a distributed intelligent control method for unmanned aerial vehicle cluster cooperative path planning, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the steps: collecting a dynamic feature set of an unmanned aerial vehicle in real time, and carrying out the time synchronization, noise filtering and formatting integration, and forming a local state data set; performing neighborhood collaborative analysis and temporary link planning on the local state data set to obtain relay chain layout data; performing environmental risk assessment and path feasibility analysis on the relay chain layout data to generate a candidate path set; adjusting the candidate path set through local obstacle avoidance and dynamic constraint to generate an executable path sequence, and generating a collaborative execution path through neighborhood communication and collaborative adjustment; performing task execution result integration and state updating on the collaborative execution path to generate group state data; and performing path feedback and loop optimization on the group state data to obtain a group collaboration result. And the task completion rate, the cooperation efficiency and the environment adaptability are improved.

The invention relates to the technical field of welding, and discloses an intelligent control method and system for the welding process of a thin-wall metal component, and the intelligent control method for the welding process of the thin-wall metal component comprises the steps that an initial welding parameter set is determined, and time synchronization is conducted; obtaining multi-source original data with timestamps in the welding process; time alignment is carried out; respectively calculating a current stability index, a heat accumulation index and a molten pool width measured value to obtain a key deviation index; based on the key deviation index, current adjustment amount, pulse parameter adjustment amount and position compensation amount are obtained through calculation; obtaining an adjustment amount set; the current welding parameters are subjected to amplitude limiting updating, and an updated current welding parameter set is obtained through a step-by-step transition method and a high-priority fast instruction; recording a welding process data log, and judging a welding state; carrying out comprehensive evaluation and data storage on the welding process; according to the method, various dynamic changes in the welding process are comprehensively coped with, and the stability and consistency of the welding quality are improved.

The invention discloses a power equipment inspection path planning method and device based on laser and vision, and relates to the technical field of intelligent robots and power inspection, and the method comprises the steps: constructing a multi-sensor system, carrying out the external parameter calibration and time synchronization of each sensor, and carrying out the environment modeling and positioning through employing an improved LIO-SAM algorithm; performing power equipment identification; generating an optimal inspection path based on the calculated coordinate set of the power equipment; according to the optimal inspection path, dynamic obstacles are detected in combination with semantic and geometric information, obstacle avoidance is achieved through local track adjustment and path re-planning, and a planned path is obtained; and driving the inspection robot to autonomously move according to the planned path, acquiring an image and infrared data at the target power equipment, and feeding back a result to the background system to complete closed-loop inspection. According to the invention, the autonomous inspection of the whole process of environment modeling, target equipment identification, path generation, dynamic obstacle avoidance and the like can be effectively realized.

The invention relates to the technical field, in particular to a power grid overhead line traveling wave recording fusion fault positioning system, which comprises signal acquisition, traveling wave event stamp capturing, zero-sequence current data flow generation, fault triggering, zero-sequence integral comparison based on time reference to generate an activation instruction, and fault recording. The characteristic topology associates and fuses the traveling wave amplitude and the zero-sequence peak value, calculates a node attenuation rate and marks a breakpoint, and the positioning solution links a double-end timestamp and a wave velocity library according to the position of the breakpoint to output a fault coordinate. According to the invention, accurate locking of the fault moment is realized through synchronous acquisition of multi-source signals; a transient window defined by traveling wave triggering ensures complete capture of features; branch identification precision is improved through current vector fusion; parameters are automatically adjusted through dynamic wave velocity matching to improve positioning accuracy; time synchronization and wave velocity compensation eliminate the influence of line parameters.

The invention discloses a multi-source navigation data fusion method for an unmanned loader, which comprises the following steps: data acquisition: acquiring environmental perception data of satellite navigation, an inertial measurement unit IMU, a wheel type odometer and a laser radar and camera in real time; performing space-time synchronization preprocessing, realizing multi-source data time synchronization, unifying environment sensing data to a body coordinate system, eliminating abnormal data, and complementing missing data; carrying out dynamic weight calculation, establishing an error model of each sensor, and adjusting a fusion weight by using an error reciprocal exponential weighting method; layering fusion is carried out, a satellite and an IMU are fused through bottom-layer extended Kalman filtering (EKF), a laser radar and a high-precision map are fused through middle-layer ICP, a middle-layer result and a wheel type odometer are integrated through high-layer federated filtering, and high-precision fusion is achieved; and outputting and optimizing a result, outputting navigation data, performing closed-loop optimization, monitoring the health degree of the sensor, and executing redundancy switching when a fault occurs. The system comprises a corresponding processing unit, and a storage medium stores a program for executing the method.

The invention provides a marine target real-time positioning and tracking system and method based on multi-source data fusion, and belongs to the field of target positioning and tracking. The sensor portion collects raw data. The data acquisition and time synchronization module is responsible for receiving and aligning data of each sensor; the multi-stage coordinate system registration and transformation module unifies all observation data to a world coordinate system by using IMU data and preset calibration parameters; the AI visual target detection module processes the video stream to identify a target; the adaptive federated Kalman filtering fusion module receives each path of processed data and outputs optimal target state estimation; and the dynamic task and resource scheduling module performs optimal allocation on system resources according to the current tracking state to form closed-loop control. Through deep fusion of high-precision RTK positioning data, laser ranging data, photoelectric pod attitude information and an AI vision algorithm, the positioning precision, tracking robustness and system real-time performance of a sea target under a complex dynamic sea condition are improved.

The invention discloses a complex environment information acquisition method based on a sensor network, and relates to the technical field of sensor networks, and the method comprises the steps: building a complex environment portrait and a service level, and generating a scene configuration comprising an information freshness budget, a risk heat map and an alarm threshold; based on scene configuration, configuring mesh wireless networking, time synchronization parameters and candidate relays, and constructing observation configuration and quality rules; and generating a data stream and a real-time queue state according to a self-adaptive sampling strategy and an event mark, calculating an emergency degree and distributing a forwarding probability according to an information freshness budget by combining a candidate relay and a risk heat map, outputting uploaded data and a quality record, completing convergence and grading alarm according to an alarm threshold, and generating a disposal feedback and audit index. The state set, the uncertainty set, the link idle degree and the energy sufficiency are linked under the constraint of the information freshness budget, so that hot spot encryption, low-risk sparseness, reduction of energy consumption and conflict rate and standard observation time efficiency are realized.

The invention discloses a cold storage temperature optimization control system and method for a chilled water storage air conditioning system, and relates to the technical field of chilled water storage air conditioning management.The method comprises the steps that 1, a plurality of operation parameters in the system operation process are collected; performing time synchronization and abnormal value elimination on the operation parameters to form a dynamic operation data set; and 2, judging the current hydraulic circulation state based on the dynamic operation data set, and outputting a circulation deviation signal when any index of the total flow of the system, the flow distribution of each branch or the temperature difference change rate of the supplied water and the returned water exceeds a set corresponding threshold value. By constructing the dynamic operation data set and monitoring key parameters such as the system temperature, the flow and the water pump operation state in real time, a unified multi-dimensional data basis is established, support is provided for follow-up operation state recognition and control strategy execution, and the real-time performance and accuracy of control are remarkably improved.

The invention discloses a solar irradiance prediction system and method based on dual attention multi-modal fusion. The method comprises the following steps: performing spatial feature extraction on a sky imaging observation map after preprocessing and time synchronization by using a MobileNetV2 network to obtain a sky imaging time sequence feature sequence; inputting the sky imaging time sequence feature sequence into a DAT model to obtain a global sky imaging time sequence feature sequence, a local sky imaging time sequence feature sequence and a local convolution position coding residual term; obtaining an aggregated sky imaging time sequence feature sequence according to the global sky imaging time sequence feature sequence, the local sky imaging time sequence feature sequence and the local convolution position coding residual item; performing time-dependent modeling on the meteorological observation data by adopting a long short-term memory network to obtain a meteorological observation data feature sequence; and performing feature fusion on the aggregated sky imaging time sequence feature sequence and the meteorological observation data feature sequence through a trans-attention mechanism to obtain a fused joint feature sequence for solar irradiance prediction.